A. Fiat, A. Goldberg, J. Hartline, and A. Karlin. Competitive Generalized Auctions. In Proceedings of the 34th Symposium on Theory of Computing, ACM Press, New York, pages 72--81, 2002.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....to these users) or maintaining a balanced budget (such that the service provider retrieves exactly the cost of providing the service) For multicasting these questions were investigated in [FPS01] in terms of the network complexity of achieving these goals) The more recent work of Fiat et.al. FGHK02] asks a di erent question are there mechanisms which maximize the pro t to the service provider This is a pro t maximizing question as opposed to the cost sharing questions above. Fiat et.al. nd mechanisms which are competitive in terms of maximizing pro t. In fact, they study the pro t ....

A. Fiat, A. Goldberg, J. Hartline, and A. Karlin. Competitive generalized auctions. STOC, 2002.

....sharing for Internet multicast transmissions. In the first paper on the topic, Herzog, Shenker, and Estrin [9] considered axiomatic and implementation aspects of the problem. Subsequently, Moulin and Shenker [14] studied the problem from a purely economic point of view. Several more recent papers [5, 2, 1, 7] adopt the distributed algorithmic mechanism design approach, which augments a gametheoretic perspective with distributed computational concerns. In this paper, we extend the results of [5] by considering a more general computational model and approximate solutions. We also extend a classic ....

....goal of the charging mechanism. If the charging mechanism were being designed by a monopoly network 4 Feigenbaum et al. operator, then one might expect the goal to be maximizing revenue. There have been some recent investigations of revenue maximizing charging schemes for multicast (see, e.g. [7]) but here we assume, as in [9, 14, 5, 2, 1] that the charging mechanism is decided by society at large (e.g. through standards bodies) or through competition. Competing network providers could not charge more than their real costs (or otherwise their prices would be undercut) nor less than ....

Fiat, A., Goldberg, A., Hartline, J., and Karlin, A. (2002). "Competitive Generalized Auctions," in Proceedings of the 34th Symposium on the Theory of Computing, pp. 72--81, ACM Press, New York.

....for the case of limited supply. The problem of designing online auctions for digital goods was first described by BarYossef et al. 3] one of a number of recent papers interested in analyzing revenue maximizing auctions without making statistical assumptions about the participating bidders [2, 6, 8, 10]. 1 Introduction While auctions are traditional and well studied economic mechanisms, the popularity of internet auctions has prompted wide interest in various aspects of auctions and related mechanisms, including the question of optimizing the total revenue of an auction. A number of recent ....

....various aspects of auctions and related mechanisms, including the question of optimizing the total revenue of an auction. A number of recent papers have addressed the design of revenue maximizing auctions without making any statistical assumptions about the bidders who participate in the auction [2, 3, 6, 8, 10]. A particularly interesting case is that of digital goods [8] goods of which infinitely many copies can be generated at no cost considered in the online setting by Bar Yossef et al. 3] In the model of Bar Yossef et al. 3] n bidders arrive in a sequence. Each bidder i is interested in ....

A. Fiat, A. Goldberg, J. Hartline, and A. Karlin. Competitive generalized auctions. In Proceedings of the 34th ACM Symposium on Theory of Computing (STOC 02), 2002.

....terms. Hence it is not possible to use numerical methods on the expected profit function itself we need to use more indirect methods. This problem is motivated by the recent work on mechanism design in general [10, 11, 8] and mechanism design for multicasting in particular [5, 7] Fiat et.al. [6] ask for mechanisms which maximize the profit to the service provider and find mechanisms which are competitive in terms of maximizing profit, but under several assumptions. We also address the profit maximization question, but in a di#erent context. We remove all the assumtions of [6] but we ....

....Fiat et.al. 6] ask for mechanisms which maximize the profit to the service provider and find mechanisms which are competitive in terms of maximizing profit, but under several assumptions. We also address the profit maximization question, but in a di#erent context. We remove all the assumtions of [6], but we assume that while the individual values of the utilities are not known to the service provider, their distribution is. On assuming knowledge of the distributions of the utilities, the problem changes to a stochastic optimization problem. Recent work in the same vein is [9] 2. A FIXED ....

A. Fiat, A. Goldberg, J. Hartline, and A. Karlin. Competitive generalized auctions. STOC, 2002.

....are public knowledge, except for the actual outcomes of the coin flips. There are several notions of truthfulness for randomized mechanisms. The strongest notion is for the mechanism to be strongly truthful This means that for every co the mechanism A is truthful. This concept has been used in [13, 3, 7, 6], but it is very restrictive. Because strong truthfulness is so restrictive, there have been various attempts to find a weaker but still useful concept. One approach is to guarantee that truthful bidding always maximizes a player s expected profit [1] i.e. the mechanism is truthful in ....

....a fair benchmark. In fact, it is well known that even when auctioning just a single copy of a single item, no truthful mechanism can always attain a guaranteed fraction of the optimal valuation, because there is no way to deal with a single astronomical bidder. Therefore, in the single item case, [7, 8, 6, 10] suggest comparing against variants of the VCG mechanism. We have shown that our auction achieves expected revenue approximately equal to that of the FVCG mechanism with a slightly reduced supply of goods. It is easy to construct a pair of examples showing that neither the VCG nor the FVCG ....

A. Fiat, A. Goldberg, J. Hartline, and A. Karlin. Competitive generalized auctions. In STOC 2002, 72-81.

....input. McAfee [12] and later Tatur [15] have shown that using the assumption that all bidders derive their values from a common distribution, it is possible to achieve almost a 1 1=n fraction of the maximum eciency by using a simple direct auction that rejects the least ecient trade. Fiat et al. [6] were the rst ones to consider the question of pro t maximization in the context of multicasting games. However, like others they dealt with the case when edge costs are known. Their mechanism either needs to be provided with the multicast tree, or it needs exponential time to construct the ....

....feasible only on tree networks. Jain and Vazirani [10] give an approximate budget balanced group strategyproof mechanism which is polynomial time for any network and also gives a cost sharing function for the nodes. Cancellable Auctions. The recent research on cancellable auctions by Fiat, et al. [6] is also of note. An auction is cancellable if the auctioneer has the option of canceling the auction if some pre speci ed criterion (such as minimum revenue) is not met, and this does not a ect the strategy of the participants. The notion of cancellability is helpful in multicast network ....

[Article contains additional citation context not shown here]

A. Fiat, A. Goldberg, J. Hartline and A. Karlin. Competitive generalized auctions. In Proc. 34th ACM Symposium on Theory of Computing, 72-81, 2002.

No context found.

A. Fiat, A. Goldberg, J. Hartline, and A. Karlin. Competitive Generalized Auctions. In Proc. 34th ACM Symposium on the Theory of Computing. ACM Press, New York, 2002.

No context found.

A. Fiat, A. Goldberg, J. Hartline, and A. Karlin. Competitive Generalized Auctions. In Proc. 34th ACM Symposium on the Theory of Computing. ACM Press, New York, 2002.

No context found.

A. Fiat, A. Goldberg, J. Hartline, and A. Karlin. Competitive Generalized Auctions. In Proc. 34th ACM Symposium on the Theory of Computing. ACM Press, New York, 2002.

No context found.

A. Fiat, A. Goldberg, J. Hartline, and A. Karlin. Competitive Generalized Auctions. In Proc. 34th ACM Symposium on the Theory of Computing. ACM Press, New York, 2002.

No context found.

A. Fiat, A. V. Goldberg, J. D. Hartline, and A. Karlin. Competitive generalized auctions. In Proc. 34rd ACM Symposium on the Theory of Computing. ACM Press, 2002. To appear.

....their own gain is to bid their true utility value. The traditional economics approach to the study of pro t maximizing auctions is to construct the optimal Bayesian auction given the prior distribution from which the bidders utility values are drawn (e.g. 3,17] In contrast, following [10,6,9], we attempt to design mechanisms that maximize pro t under any market conditions. As in competitive analysis of online algorithms, we gauge a truthful double auction mechanism s performance on a particular bid set by comparing it against the pro t that would be achieved by an optimal auction, ....

.... 2, from Equations (1) and (2) we have Note that becaus A is competitive, the expected revenue from Step 3 and Step 4 are F ) 2 and F ) 2 respectively. Thus, E[MA (b; s) F ) F (b; s) Plugging in the 4 competitive Sampling Cost Sharing auction [6], we get a double auction with a competitive ratio of 8. Plugging in the 3.39 competitive Consensus Revenue Estimate auction [8] we get a competitive ratio of 6:78. We can do better if we customize mechanisms for the double auction problem. 5 The Revenue Extraction and Estimation Technique In ....

[Article contains additional citation context not shown here]

A. Fiat, A. V. Goldberg, J. D. Hartline, and A. Karlin. Competitive generalized auctions. In Proc. 34rd ACM Symposium on the Theory of Computing. ACM Press, 2002. To appear.

....is bounded by #. An auction is truthful with high probability if # tends to zero with some parameter in the input, e.g. the number of winners in an optimal auction. For other solution concepts related to approximate or probabilistic truthfulness, see for example [10, 11] The previous work of [5, 6, 7] considered the design of truthful auction mechanisms for maximizing the profit of the seller under unknown market conditions. In contrast to the traditional approach from economics to profit maximization, which is to give an average case (Bayesian) analysis of an auction that is endowed with ....

....uninformed auction is measured by comparing it to the performance of an optimal auction that is completely informed. In this setting they gave truthful auctions that are competitive, i.e. that always obtain a constant factor of the profit of an optimal informed auction. This work was extended in [5, 6] to improve the constant factor using new auction design and analysis techniques. Our analysis di#ers from that of Goldberg et al. in that we assume that we are in the mass market case and that it is always desirable to sell many items. Unfortunately, all known competitive auctions have the ....

[Article contains additional citation context not shown here]

A. Fiat, A. Goldberg, J. Hartline, and A. Karlin. Competitive Generalized Auctions. In Proc. 34th ACM Symposium on the Theory of Computing. ACM Press, New York, 2002.

....competitive ratios which approach 1 as m increases. A more elegant result would be a single, non parameterized auction that has the same property. An interesting question is how to extend the competitive framework introduced in this paper to other mechanism design problems. A followup paper [5] makes progress in this direction by introducing the concept of a cancellable auction, a competitive auction that can be cancelled if its revenue fails to meet a target. The paper shows that the DSOT auction becomes untruthful if cancelling is allowed while the SCS auction remains truthful, i.e. ....

A. Fiat, A. Goldberg, J. Hartline, and A. Karlin. Competitive Generalized Auctions. In Proc. 34th ACM Symposium on the Theory of Computing. ACM Press, New York, 2002.

No context found.

A. Fiat, A. Goldberg, J. Hartline, and A. Karlin. Competitive Generalized Auctions. In Proceedings of the 34th Symposium on Theory of Computing, ACM Press, New York, pages 72--81, 2002.

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A. Fiat, A. Goldberg, J. Hartline, and A. Karlin. Competitive generalized auctions. In Proceedings of the 34th ACM Symposium on Theory of Computing (STOC 02), 2002.

No context found.

A. Fiat, A. Goldberg, J. Hartline, and A. Karlin. Competitive Generalized Auctions. STOC, 2002.

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A. Fiat, A. Goldberg, J. Hartline, and A. Karlin. Competitive generalized auctions. In Proceedings of the 34th ACM Symposium on Theory of Computing (STOC), pages 72--81, 2002.

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A.Fiat, A. Goldberg, J. Hartline, and A. Karlin, Competitive generalized auctions. In Proceedings of the 34th Annual ACM Symposium on Theory of Computation, pages 72-81, 2002

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A.FIAT, A. GOLDBERG, J. HARTLINE, AND A. KARLIN, Competitive generalized auctions. In Proceedings of the 34th Annual ACM Symposium on Theory of Computation, pages 72--81, 2002

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Amos Fiat, Andrew Goldberg, Jason Hartline, and Anna Karlin. Competitive generalized auctions. In Proc. of the 34th ACM Symposium on Theory of Computing (STOC'02), 2002.

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Fiat A., Goldberg A., Hartline J. and Karlin A., Competitive Generalized Auctions, STOC 2002.

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A. Fiat, A. Goldberg, J. Hartline, and A. Karlin, \Competitive Generalized Auctions," in Proceedings of the 34th ACM Symposium on Theory of Computing, ACM Press, New York, pages 72-81, 2002.

No context found.

Amos Fiat, Andrew Goldberg, Jason Hartline, and Anna Karlin. Competitive generalized auctions. In Proc. of the 34th ACM Symposium on Theory of Computing (STOC'02), 2002.

No context found.

A. Fiat, A. Goldberg, J. Hartline, and A. Karlin. Competitive Generalized Auctions. In Proceedings of the 34th Symposium on Theory of Computing, ACM Press, New York, pages 72--81, 2002.

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